THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.

Binary butterfly optimization approaches for feature selection

https://link.springer.com/content/pdf/10.1007/s42235-021-0050-y.pdf

The Colony Predation Algorithm

Book review: Applied cryptography: Protocols, algorithms, and source code in C

Protein structural class could provide important clues for understanding protein fold, evolution and function. However, it is still a challenging problem to accurately predict protein structural classes for low-similarity sequences. This paper was devoted to develop a powerful method to predict protein structural classes for low-similarity sequences. On the basis of a very objective and strict benchmark dataset, we firstly extracted optimal tripeptide compositions (OTC) which was picked out by using feature selection technique to formulate protein samples. And an overall accuracy of 91.1% was achieved in jackknife cross-validation. Subsequently, we investigated the accuracies of three popular features: position-specific scoring matrix (PSSM), predicted secondary structure information (PSSI) and the average chemical shift (ACS) for comparison. Finally, to further improve the prediction performance, we examined all combinations of the four kinds of features and achieved the maximum accuracy of 96.7% in jackknife cross-validation by combining OTC with ACS, demonstrating that the model is efficient and powerful. Our study will provide an important guide to extract valuable information from protein sequences.

Predicting protein structural classes for low-similarity sequences by evaluating different features

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, individuals in MBO are updated. MBO can outperform many state-of-the-art optimization techniques when solving global numerical optimization and engineering problems. This paper presents a comprehensive review of the MBO algorithm including its modifications, hybridizations, variants, and applications. Additionally, further research directions for MBO are discussed. This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm.

Monarch butterfly optimization: A comprehensive review

This paper presents a novel binary monarch butterfly optimization (BMBO) method, intended for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued vectors and binary vectors, are used to represent the monarch butterfly individuals in BMBO. Real-valued vectors constitute the search space, whereas binary vectors form the solution space. In other words, monarch butterfly optimization works directly on real-valued vectors, while solutions are represented by binary vectors. Three kinds of individual allocation schemes are tested in order to achieve better performance. Toward revising the infeasible solutions and optimizing the feasible ones, a novel repair operator, based on greedy strategy, is employed. Comprehensive numerical experimentations on three types of 0–1 KP instances are carried out. The comparative study of the BMBO with four state-of-the-art classical algorithms clearly points toward the superiority of the former in terms of search accuracy, convergent capability and stability in solving the 0–1 KP, especially for the high-dimensional instances.

Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization

The discounted {0-1} knapsack problem (DKP) extends the classical 0-1 knapsack problem (0-1 KP) in which a set of item groups is included and each group consists of three items, whereas at most one of the three items can be packed into the knapsack. Therefore, the DKP is more complicated and computationally difficult than 0-1 KP. The DKP has been found many applications in real economic problems and other areas. In this paper, the influence of Levy flights operator and fly straightly operator in the moth search (MS) algorithm is verified. Nine types of new mutation operator based on the global harmony search are specially devised to replace Levy flights operator. Then, nine novel MS-based algorithms for DKP are proposed (denoted by MS1–MS9). Extensive experiments on three sets of 30 DKP instances demonstrate the remarkable performance of the proposed nine new MS-based approaches. In particular, it discovers that MS1–MS3 show better comprehensive performance among 10 algorithms. A variety of analyses indicate the important contribution of the individual of memory consideration in MS1–MS9.

Binary Moth Search Algorithm for Discounted {0-1} Knapsack Problem

As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack problem (DKP) is proposed based on the concept of discount in the commercial world. The DKP contains a set of item groups where each group includes three items, whereas no more than one item in each group can be packed in the knapsack, which makes it more complex and challenging than 0-1 KP. At present, the main two algorithms for solving the DKP include exact algorithms and approximate algorithms. However, there are some topics which need to be further discussed, i.e., the improvement of the solution quality. In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed. In MMBO, two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO. Experimental analyses show that the first strategy can enhance the global search ability, while the second strategy can strengthen local search ability and prevent premature convergence during the evolution process. Based on this, MBO is combined with each strategy, denoted as NCMBO and GMMBO, respectively. We compared MMBO with other six methods, including NCMBO, GMMBO, MBO, FirEGA, SecEGA and elephant herding optimization. The experimental results on three types of large-scale DKP instances show that NCMBO, GMMBO and MMBO are all suitable for solving DKP. In addition, MMBO outperforms other six methods and can achieve a good approximate solution with its approximation ratio close to 1 on almost all the DKP instances.

Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem

Recently, inspired by the migration behavior of monarch butterflies in nature, a metaheuristic optimization algorithm, called monarch butterfly optimization (MBO), was proposed. In the present study, a novel chaotic MBO algorithm (CMBO) is proposed, in which chaos theory is introduced in order to enhance its global optimization ability. Here, 12 one-dimensional classical chaotic maps are used to tune two main migration processes of monarch butterflies. Meanwhile, applying Gaussian mutation operator to some worst individuals can effectively prevent premature convergence of the optimization process. The performance of CMBO is verified and analyzed by three groups of large-scale 0–1 knapsack problems instances. The results show that the introduction of appropriate chaotic map and Gaussian perturbation can significantly improve the solution quality together with the overall performance of the proposed CMBO algorithm. The proposed CMBO can outperform the standard MBO and other eight state-of-the-art canonical algorithms.

Yanhong Feng

Papers

Monarch butterfly optimization: A comprehensive review

Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization

Binary Moth Search Algorithm for Discounted {0-1} Knapsack Problem

Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem

Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation